slashtechno
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wyzely-detect
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Merge pull request #6 from slashtechno/publish-to-pypi 

Prepare to publish to PyPi
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slashtechno 2023-10-22 16:57:45 -05:00 committed by GitHub
parent 4f8e26b2a2 259cc8f200
commit 3235bb61bb
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15 changed files with 440 additions and 295 deletions
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39
.github/workflows/python-publish.yml vendored Normal file
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@ -0,0 +1,39 @@
# This workflow will upload a Python Package using Twine when a release is created
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
# This workflow uses actions that are not certified by GitHub.
# They are provided by a third-party and are governed by
# separate terms of service, privacy policy, and support
# documentation.
name: Upload Python Package
on:
release:
types: [published]
permissions:
contents: read
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
with:
python-version: '3.x'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install build
- name: Build package
run: python -m build
- name: Publish package
uses: pypa/gh-action-pypi-publish@v1.8.10
with:
user: __token__
password: ${{ secrets.PYPI_PROJECT_API_TOKEN }}

6
.gitignore vendored
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@ -2,7 +2,9 @@
config/ config/
using_yolov8.ipynb using_yolov8.ipynb
yolov8n.pt yolov8n.pt
.venv/ *venv/
__pycache__/ __pycache__/
faces/* faces/*
!faces/.gitkeep !faces/.gitkeep
dist/
test.txt

8
.vscode/launch.json vendored
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@ -5,11 +5,13 @@
"version": "0.2.0", "version": "0.2.0",
"configurations": [ "configurations": [
{ {
"name": "Python: Module", // "name": "Python: Module",
"name": "Debug Wyzely Detect",
"type": "python", "type": "python",
"request": "launch", "request": "launch",
"module": "set_detect_notify", "module": "wyzely_detect",
"justMyCode": true // "justMyCode": true
"justMyCode": false
} }
] ]
} }

3
.vscode/settings.json vendored Normal file
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@ -0,0 +1,3 @@
{
"files.eol": "\n"
}

2
Dockerfile
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@ -9,4 +9,4 @@ COPY . .
RUN poetry install RUN poetry install
ENTRYPOINT ["poetry", "run", "python", "-m", "set_detect_notify"] ENTRYPOINT ["poetry", "run", "python", "-m", "wyzely_detect"]

25
README.md
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@ -1,5 +1,5 @@
# Set, Detect, Notify # Wyzely Detect
Recognize faces/objects in (Wyze Cam) footage and send notifications to your phone (or other devices) Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices
### Features ### Features
- Recognize objects - Recognize objects
@ -13,21 +13,22 @@ Recognize faces/objects in (Wyze Cam) footage and send notifications to your pho
## Prerequisites ## Prerequisites
### Poetry/Python ### Poetry/Python
- Camera, either a webcam or a Wyze Cam - Camera, either a webcam or a Wyze Cam
- All RTSP feeds _should_ work, however. - All RTSP feeds _should_ work, however.
- Python - Python 3.10 or 3.11
- Poetry - Poetry
### Docker ### Docker
- A Wyze Cam - A Wyze Cam
- Any other RTSP feed _should_ work, as mentioned above - Any other RTSP feed _should_ work, as mentioned above
- Python - Docker
- Poetry - Docker Compose
## What's not required ## What's not required
- A Wyze subscription - A Wyze subscription
## Usage ## Usage
### Installation ### Installation
1. Clone this repo with `git clone https://github.com/slashtechno/wyze-face-recognition.git` 1. Clone this repo with `git clone https://github.com/slashtechno/wyzely-detect`
2. `cd` into the cloned repository 2. `cd` into the cloned repository
3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker 3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
@ -37,7 +38,7 @@ Recognize faces/objects in (Wyze Cam) footage and send notifications to your pho
#### Poetry #### Poetry
1. `poetry install` 1. `poetry install`
2. `poetry run -- set-detect-notify` 2. `poetry run -- wyzely-detect`
### Configuration ### Configuration
The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker. The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.

62
deepface-test.ipynb
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@ -36,12 +36,12 @@
"# cv2.imwrite(str(uuid_path), frame)\n", "# cv2.imwrite(str(uuid_path), frame)\n",
"# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n", "# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
"# Don't throw an error if no face is detected (enforce_detection=False)\n", "# Don't throw an error if no face is detected (enforce_detection=False)\n",
"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False)\n", "dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
"# Get the identity of the person\n", "# Get the identity of the person\n",
"for i, pd_dataframe in enumerate(dfs):\n", "for i, pd_dataframe in enumerate(dfs):\n",
" # Sort the dataframe by confidence\n", " # Sort the dataframe by confidence\n",
" # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n", " # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
" pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n", " # pd_dataframe.sort_values(by=['model_name=\"ArcFace\", detector_backend=\"opencv\")'], inplace=True, ascending=False)\n",
" print(f'On dataframe {i}')\n", " print(f'On dataframe {i}')\n",
" print(pd_dataframe)\n", " print(pd_dataframe)\n",
" # Get the most likely identity\n", " # Get the most likely identity\n",
@ -49,7 +49,7 @@
" # We could use Path to get the parent directory of the image to use as the identity\n", " # We could use Path to get the parent directory of the image to use as the identity\n",
" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n", " print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
" # Get the most likely identity's confidence\n", " # Get the most likely identity's confidence\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n", " print(f'Confidence: {pd_dataframe.iloc[0][\"ArcFace_cosine\"]}')\n",
"\n", "\n",
"# uuid_path.unlink()" "# uuid_path.unlink()"
] ]
@ -67,7 +67,61 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"DeepFace.stream(db_path=\"faces\")" "DeepFace.stream(db_path=\"faces\", model_name=\"ArcFace\", detector_backend=\"opencv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stream normal frame by frame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from deepface import DeepFace\n",
"import cv2\n",
"from pathlib import Path\n",
"import uuid\n",
"import pandas as pd\n",
"\n",
"def main():\n",
" cap = cv2.VideoCapture(0)\n",
" while True:\n",
" ret, frame = cap.read()\n",
" dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
" for i, pd_dataframe in enumerate(dfs):\n",
" print(f'On dataframe {i}')\n",
" print(pd_dataframe)\n",
" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"ArcFace_cosine\"]}')\n",
" cv2.imshow(\"frame\", frame)\n",
" if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n",
" break\n",
" cap.release()\n",
" cv2.destroyAllWindows()\n",
" \n",
"\n",
"\n",
"main()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other functions\n"
] ]
} }
], ],

15
docker-compose.yml
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@ -3,7 +3,7 @@ networks:
all: all:
services: services:
bridge: bridge:
container_name: bridge-wyze container_name: bridge-wyzely-detect
restart: unless-stopped restart: unless-stopped
image: mrlt8/wyze-bridge:latest image: mrlt8/wyze-bridge:latest
# I think we can remove the ports, since we're using the network # I think we can remove the ports, since we're using the network
@ -18,12 +18,9 @@ services:
- WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password - WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password
networks: networks:
all: all:
# aliases:
# - bridge
# - wyze-bridge
ntfy: ntfy:
image: binwiederhier/ntfy image: binwiederhier/ntfy
container_name: ntfy-wyze container_name: ntfy-wyzely-detect
command: command:
- serve - serve
environment: environment:
@ -36,10 +33,10 @@ services:
restart: unless-stopped restart: unless-stopped
networks: networks:
all: all:
facial_recognition: wyzely-detect:
container_name: face-recognition-wyze container_name: wyzely-detect
restart: unless-stopped restart: unless-stopped
# image: ghcr.io/slashtechno/wyze_face_recognition:latest # image: ghcr.io/slashtechno/wyzely-detect:latest
build: build:
context: . context: .
dockerfile: Dockerfile dockerfile: Dockerfile
@ -50,7 +47,7 @@ services:
environment: environment:
- URL=rtsp://bridge:8554/cv - URL=rtsp://bridge:8554/cv
- NO_DISPLAY=true - NO_DISPLAY=true
- NTFY_URL=http://ntfy:80/set-detect-notify - NTFY_URL=http://ntfy:80/wyzely-detect
depends_on: depends_on:
- bridge - bridge

463
poetry.lock generated
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@ -124,33 +124,29 @@ lxml = ["lxml"]
[[package]] [[package]]
name = "black" name = "black"
version = "23.9.1" version = "23.10.0"
description = "The uncompromising code formatter." description = "The uncompromising code formatter."
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[package.dependencies] [package.dependencies]
@ -528,13 +524,13 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
[[package]] [[package]]
name = "cycler" name = "cycler"
version = "0.12.0" version = "0.12.1"
description = "Composable style cycles" description = "Composable style cycles"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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[package.extras] [package.extras]
@ -723,53 +719,53 @@ files = [
[[package]] [[package]]
name = "fonttools" name = "fonttools"
version = "4.43.0" version = "4.43.1"
description = "Tools to manipulate font files" description = "Tools to manipulate font files"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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[package.dependencies] [package.dependencies]
numpy = ">=1.18.5,<1.26.0" numpy = ">=1.21.6,<1.28.0"
[package.extras] [package.extras]
dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"] dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"] doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
[[package]] [[package]]
name = "seaborn" name = "seaborn"
@ -3432,13 +3441,13 @@ files = [
[[package]] [[package]]
name = "ultralytics" name = "ultralytics"
version = "8.0.193" version = "8.0.200"
description = "Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification." description = "Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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[package.dependencies] [package.dependencies]
@ -3459,18 +3468,18 @@ torchvision = ">=0.9.0"
tqdm = ">=4.64.0" tqdm = ">=4.64.0"
[package.extras] [package.extras]
dev = ["check-manifest", "coverage", "ipython", "mkdocs-material", "mkdocs-redirects", "mkdocs-ultralytics-plugin (>=0.0.29)", "mkdocstrings[python]", "pytest", "pytest-cov"] dev = ["check-manifest", "coverage", "ipython", "mkdocs-material", "mkdocs-redirects", "mkdocs-ultralytics-plugin (>=0.0.29)", "mkdocstrings[python]", "pre-commit", "pytest", "pytest-cov"]
export = ["coremltools (>=7.0)", "openvino-dev (>=2023.0)", "tensorflow (<=2.13.1)", "tensorflowjs"] export = ["coremltools (>=7.0)", "openvino-dev (>=2023.0)", "tensorflow (<=2.13.1)", "tensorflowjs"]
[[package]] [[package]]
name = "urllib3" name = "urllib3"
version = "2.0.6" version = "2.0.7"
description = "HTTP library with thread-safe connection pooling, file post, and more." description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.7"
files = [ files = [
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@ -3618,4 +3627,4 @@ files = [
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.10, <3.12" python-versions = ">=3.10, <3.12"
content-hash = "952e41ab099f9d1c7055465d5a3c228df677c422d20dea8ff8d558d2e4d8f2be" content-hash = "6712094a7e402c4587037939b7a33ad4c2463a545c222374bc471925c33242d9"

22
pyproject.toml
View File

@ -1,15 +1,19 @@
[tool.poetry] [tool.poetry]
name = "set_detect_notify" name = "wyzely-detect"
version = "0.1.0" version = "0.1.0"
description = "Detect all the things" description = "Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices"
authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"] authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
repository = "https://github.com/slashtechno/wyzely-detect"
keywords = ["object-detection", "face-detection", "wyze", "security", "yolov8", "unified-push"]
license = "MIT" license = "MIT"
readme = "README.md" readme = "README.md"
packages = [{include = "set_detect_notify"}] packages = [{include = "wyzely_detect"}]
[tool.poetry.dependencies] [tool.poetry.dependencies]
# python = "^3.10"
# Works on 3.10 and 3.11, at least in my testing
python = ">=3.10, <3.12" python = ">=3.10, <3.12"
python-dotenv = "^1.0.0" python-dotenv = "^1.0.0"
httpx = "^0.25.0" httpx = "^0.25.0"
opencv-python = "^4.8.1.78" opencv-python = "^4.8.1.78"
@ -20,10 +24,15 @@ numpy = "^1.23.2"
# https://github.com/python-poetry/poetry/issues/6409 # https://github.com/python-poetry/poetry/issues/6409
torch = ">=2.0.0, !=2.0.1, !=2.1.0" torch = ">=2.0.0, !=2.0.1, !=2.1.0"
# https://stackoverflow.com/a/76477590/18270659
# https://discuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
# Might be able to remove this version constraint later
tensorflow-io-gcs-filesystem = "0.31.0" tensorflow-io-gcs-filesystem = "0.31.0"
deepface = "^0.0.79" tensorflow = "^2.14.0"
deepface = "^0.0.79"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
black = "^23.9.1" black = "^23.9.1"
ruff = "^0.0.291" ruff = "^0.0.291"
@ -41,6 +50,7 @@ build-backend = "poetry.core.masonry.api"
# Where possible, `black` will attempt to format to 88 characters # Where possible, `black` will attempt to format to 88 characters
# However, setting ruff to 135 will allow for longer lines that can't be auto-formatted # However, setting ruff to 135 will allow for longer lines that can't be auto-formatted
line-length = 135 line-length = 135
extend-select= ["FIX002"]
[tool.poetry.scripts] [tool.poetry.scripts]
set-detect-notify = "set_detect_notify.__main__:main" wyzely-detect = "wyzely_detect.__main__:main"

0
set_detect_notify/__init__.py → wyzely_detect/__init__.py
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48
set_detect_notify/__main__.py → wyzely_detect/__main__.py
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@ -33,9 +33,11 @@ def main():
else: else:
print("No .env file found") print("No .env file found")
# TODO: If possible, move the argparse stuff to a separate file
# It's taking up too many lines in this file
argparser = argparse.ArgumentParser( argparser = argparse.ArgumentParser(
prog="Detect It", prog="Wyzely Detect",
description="Detect it all!", description="Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices", # noqa: E501
epilog=":)", epilog=":)",
) )
@ -87,7 +89,7 @@ def main():
if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != "" if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != ""
else "faces", else "faces",
type=str, type=str,
help="The directory to store the faces. Should contain 1 subdirectory of images per person", help="The directory to store the faces. Can either contain images or subdirectories with images, the latter being the preferred method", # noqa: E501
) )
argparser.add_argument( argparser.add_argument(
"--detect-object", "--detect-object",
@ -118,13 +120,13 @@ def main():
# Defaults for the stuff here and down are already set in notify.py. # Defaults for the stuff here and down are already set in notify.py.
# Setting them here just means that argparse will display the default values as defualt # Setting them here just means that argparse will display the default values as defualt
# TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x # TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x
# TODO: Make ntfy optional in ntfy.py. Currently, unless there is a local or LAN instance of ntfy, this can't run offline
notifcation_services = argparser.add_argument_group("Notification Services") notifcation_services = argparser.add_argument_group("Notification Services")
notifcation_services.add_argument( notifcation_services.add_argument(
"--ntfy-url", "--ntfy-url",
default=os.environ["NTFY_URL"] default=os.environ["NTFY_URL"]
if "NTFY_URL" in os.environ and os.environ["NTFY_URL"] != "" if "NTFY_URL" in os.environ and os.environ["NTFY_URL"] != ""
else "https://ntfy.sh/set-detect-notify", else "https://ntfy.sh/wyzely-detect",
type=str, type=str,
help="The URL to send notifications to", help="The URL to send notifications to",
) )
@ -158,7 +160,7 @@ def main():
args = argparser.parse_args() args = argparser.parse_args()
# Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu # Check if a CUDA GPU is available. If it is, set it via torch. If not, set it to cpu
# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168 # https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
# Currently, I have been unable to set up Poetry to use GPU for Torch # Currently, I have been unable to set up Poetry to use GPU for Torch
for i in range(torch.cuda.device_count()): for i in range(torch.cuda.device_count()):
@ -198,6 +200,10 @@ def main():
# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale) # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
results = model(run_frame, verbose=False) results = model(run_frame, verbose=False)
path_to_faces = Path(args.faces_directory)
path_to_faces_exists = path_to_faces.is_dir()
for i, r in enumerate(results): for i, r in enumerate(results):
# list of dicts with each dict containing a label, x1, y1, x2, y2 # list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = [] plot_boxes = []
@ -205,19 +211,23 @@ def main():
# The following is stuff for people # The following is stuff for people
# This is still in the for loop as each result, no matter if anything is detected, will be present. # This is still in the for loop as each result, no matter if anything is detected, will be present.
# Thus, there will always be one result (r) # Thus, there will always be one result (r)
if face_details := utils.recognize_face(
path_to_directory=Path(args.faces_directory), run_frame=run_frame # Only run if path_to_faces exists
): # May be better to check every iteration, but this also works
plot_boxes.append(face_details) if path_to_faces_exists:
objects_and_peoples = notify.thing_detected( if face_details := utils.recognize_face(
thing_name=face_details["label"], path_to_directory=path_to_faces, run_frame=run_frame
objects_and_peoples=objects_and_peoples, ):
detection_type="peoples", plot_boxes.append(face_details)
detection_window=args.detection_window, objects_and_peoples = notify.thing_detected(
detection_duration=args.detection_duration, thing_name=face_details["label"],
notification_window=args.notification_window, objects_and_peoples=objects_and_peoples,
ntfy_url=args.ntfy_url, detection_type="peoples",
) detection_window=args.detection_window,
detection_duration=args.detection_duration,
notification_window=args.notification_window,
ntfy_url=args.ntfy_url,
)
# The following is stuff for objects # The following is stuff for objects
# Setup dictionary of object names # Setup dictionary of object names

0
set_detect_notify/utils/__init__.py → wyzely_detect/utils/__init__.py
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2
set_detect_notify/utils/notify.py → wyzely_detect/utils/notify.py
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@ -32,7 +32,7 @@ def thing_detected(
detection_window: int = 15, detection_window: int = 15,
detection_duration: int = 2, detection_duration: int = 2,
notification_window: int = 15, notification_window: int = 15,
ntfy_url: str = "https://ntfy.sh/set-detect-notify", ntfy_url: str = "https://ntfy.sh/wyzely-detect",
) -> dict: ) -> dict:
""" """
A function to make sure 2 seconds of detection is detected in 15 seconds, 15 seconds apart. A function to make sure 2 seconds of detection is detected in 15 seconds, 15 seconds apart.

40
set_detect_notify/utils/utils.py → wyzely_detect/utils/utils.py
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@ -74,6 +74,7 @@ def recognize_face(
In addition, accepts an opencv image to be used as the frame to be searched In addition, accepts an opencv image to be used as the frame to be searched
Returns a single dictonary as currently only 1 face can be detected in each frame Returns a single dictonary as currently only 1 face can be detected in each frame
Cosine threshold is 0.3, so if the confidence is less than that, it will return None
dict contains the following keys: label, x1, y1, x2, y2 dict contains the following keys: label, x1, y1, x2, y2
The directory should be structured as follows: The directory should be structured as follows:
faces/ faces/
@ -92,37 +93,52 @@ def recognize_face(
""" """
global first_face_try global first_face_try
# If it's the first time the function is being run, remove representations_vgg_face.pkl, if it exists # If it's the first time the function is being run, remove representations_arcface.pkl, if it exists
if first_face_try: if first_face_try:
try: try:
Path("representations_vgg_face.pkl").unlink() path_to_directory.joinpath("representations_arcface.pkl").unlink()
print("Removing representations_vgg_face.pkl") print("Removing representations_arcface.pkl")
except FileNotFoundError: except FileNotFoundError:
pass print("representations_arcface.pkl does not exist")
first_face_try = False first_face_try = False
# face_dataframes is a vanilla list of dataframes # face_dataframes is a vanilla list of dataframes
# It seems face_dataframes is empty if the face database (directory) doesn't exist. Seems to work if it's empty though
# This line is here to prevent a crash if that happens. However, there is a check in __main__ so it shouldn't happen
face_dataframes = []
try: try:
face_dataframes = DeepFace.find( face_dataframes = DeepFace.find(
run_frame, run_frame,
db_path=str(path_to_directory), db_path=str(path_to_directory),
enforce_detection=True, # Problem with enforce_detection=False is that it will always (?) return a face, no matter the confidence
# Thus, false-positives need to be filtered out
enforce_detection=False,
silent=True, silent=True,
# Could use VGG-Face, but whilst fixing another issue, ArcFace seemed to be slightly faster
# I read somewhere that opencv is the fastest (but not as accurate). Could be changed later, but opencv seems to work well
model_name="ArcFace",
detector_backend="opencv",
) )
except ValueError as e: except ValueError as e:
if ( if (
str(e) str(e)
== "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False." == "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False." # noqa: E501
): ):
# print("No faces recognized") # For debugging
return None return None
else:
raise e
# Iteate over the dataframes # Iteate over the dataframes
for df in face_dataframes: for df in face_dataframes:
# The last row is the highest confidence # The last row is the highest confidence
# So we can just grab the path from there # So we can just grab the path from there
# iloc = Integer LOCation # iloc = Integer LOCation
path_to_image = Path(df.iloc[-1]["identity"]) path_to_image = Path(df.iloc[-1]["identity"])
# Get the name of the parent directory # If the parent name is the same as the path to the database, then set label to the image name instead of the parent directory name
label = path_to_image.parent.name if path_to_image.parent == Path(path_to_directory):
label = path_to_image.name
else:
label = path_to_image.parent.name
# Return the coordinates of the box in xyxy format, rather than xywh # Return the coordinates of the box in xyxy format, rather than xywh
# This is because YOLO uses xyxy, and that's how plot_label expects # This is because YOLO uses xyxy, and that's how plot_label expects
# Also, xyxy is just the top left and bottom right corners of the box # Also, xyxy is just the top left and bottom right corners of the box
@ -132,9 +148,11 @@ def recognize_face(
"x2": df.iloc[-1]["source_x"] + df.iloc[-1]["source_w"], "x2": df.iloc[-1]["source_x"] + df.iloc[-1]["source_w"],
"y2": df.iloc[-1]["source_y"] + df.iloc[-1]["source_h"], "y2": df.iloc[-1]["source_y"] + df.iloc[-1]["source_h"],
} }
# After some brief testing, it seems positve matches are > 0.3 # After some brief testing, it seems positive matches are > 0.3
# I have not seen any false positives, so there is no threashold yet distance = df.iloc[-1]["ArcFace_cosine"]
distance = df.iloc[-1]["VGG-Face_cosine"] # TODO: Make this a CLI argument
if distance < 0.3:
return None
# if 0.5 < distance < 0.7: # if 0.5 < distance < 0.7:
# label = "Unknown" # label = "Unknown"
to_return = dict(label=label, **coordinates) to_return = dict(label=label, **coordinates)